In this study, we evaluated the performance of a deep network called LPRNet [1]}. We performed tests on two license plates datasets. In addition, we compared its performance against Tesseract [2]}. While LPRNet was designed to recognize characters from license plates, Tesseract is generally used to recognize characters from documents. Since we had a synthetic dataset available for tests, we selected Tesseract as a baseline for comparison, at least on the synthetic images. We were also interested in testing if LPRNet was able to maintain its performance when trained on a different dataset, given that license plates on each country have unique characteristics. We assumed that a robust system should recognize characters independently of the dataset in use if adequately trained. Besides, we expected LPRNet to be similarly good either on a real and a synthetic dataset. We evaluated recognition accuracy using a test dataset containing \(1\,000\)  real license plates images [1]} and a subset from a dataset of synthetic license plates images [4]}, with \(10\,000\)  images for training and \(1\,000\)  images for testing.
